Product feature and relation comparison system

• US 7,246,110 B1
• Filed: 05/25/2000
• Issued: 07/17/2007
• Est. Priority Date: 05/25/2000
• Status: Expired due to Term

• Current Assignee: CBS Interactive Inc. (Paramount Global (f/k/a ViacomCBS Inc.))
• Original Assignee: CNET Networks Incorporated (Paramount Global (f/k/a ViacomCBS Inc.))
• Inventors: Musgrove, Timothy A., Walsh, Robin
• Primary Examiner(s): RUDY, ANDREW J
• Application Number:
  US09/579,859
• Time in Patent Office: 2,609 Days
• Field of Search: 705/27, 705 7- 10, 705/28, 707/3, 707/5, 707/7, 707/10, 707/100
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching

Abstract

A method of presenting data regarding products. Feature categories are assigned to each product category based on available data. A weighted importance is assigned to each feature category of each product category based on the available data. The items in the product category are ranked according to the weighted importance of the feature categories of each item, and the evaluation metrics of each feature category.

Claims

1. A method of ranking products of a plurality of different brands based upon data records stored in at least one computer readable storage medium and including data indicating product features and values corresponding to the product features, said method comprising:

assigning a plurality of feature categories corresponding to a plurality of product features for at least one product category based on available data in the data records;

assigning a weighted importance to the plurality of feature categories based on available data in the data records;

assigning evaluative metrics to each feature in each feature category;

ranking products of the plurality of different brands in the product category according to the weighted importance of the feature categories included in each item and based on each product'"'"'s evaluative metrics;

receiving a plurality of selections from a user indicating a plurality of feature categories of importance to the user for the product category;

assigning an increased weighted importance of the selected feature categories;

generating a customized ranking of products in the product category ranked based on the weighted importance of each feature categories, the increased weighted importance of the selected feature categories, and each product'"'"'s evaluative metrics;

providing to the user, the generated customized ranking of products in the product category;

assigning a tag to each essential feature category of each product category based on available data;

determining whether each product category includes all of the feature categories having the tag of another product category, and includes at least one feature category that the other product category does not have;

assigning a relation type of kind to at least one product category based on said determination;

labeling the at least one product category as a child category of the other product category; and

labeling the other product category as a parent category of the child product category.

2. The method of claim 1 wherein assigning a weighted importance to each feature category comprises assigning a score between 0 and 100 to each feature category in a product category.

3. The method of claim 1 wherein the products in each product category are products offered for sale on the internet.

4. The method of claim 1 further comprising:

creating links within each product category to reflect the assigned relation; and

using the assigned relation to create at least one hierarchical product category tree wherein each hierarchical category tree has a heading product category that is only a parent category and changing the assigned weighted importance of a feature in the parent category changes the assigned weighted importance of the feature in the child category.

5. The method of claim 4 wherein the assigned weighted importance of a feature in a child category overrides weighted importance assigned to the feature in the child category'"'"'s parent category.

6. The method of claim 1 further comprising:

assigning a property type to each feature category;

wherein said step of assigning the evaluative metrics to each feature in each feature category is based on the feature category property type and available data unless the property type is a discrete property type.

7. The method of claim 1 wherein the products in each product category are products offered for sale by merchants.

8. The method of claim 6 wherein assigning the property type comprises:

assigning a numerical property type to a feature category if the features in the feature category are measured in a quantitative way;

assigning an enumerated property type to a feature category if the features in the feature category have a fixed number of specified values including

assigning a sub-property type of discrete if one feature in a feature category is not inherently more valuable than another,

assigning a sub-property type of scalar if one feature in a feature category is inherently more valuable than another

assigning a sub-property type of Boolean if the features in a feature category may have a valuation of only yes or no;

assigning a sub-property type of qualified Boolean if the feature in a feature category may have a valuation of yes, no, or optional; and

assigning a property type of text property if the features in the feature category are represented by free form text.

9. The method of claim 6 further comprising:

assigning a special meta-tag to a cluster of feature categories based on groupings derived from the data records if the features of the categories may have a value consisting of one of;

yes, no or optional.

10. The method of claim 9 further comprising:

ranking items within a product category by the number of feature categories represented in an item within a cluster of feature categories.

11. The method of claim 6 further comprising:

assigning a evaluative tag of forward metric to a feature category if a value of an item in a product category increases as a numerical valuation of features within the feature category increases based on available data;

assigning a evaluative tag of backward metric based on available data to a feature category if the value of an item in a product category decreases as numerical valuation of features within the feature category increases; and

assigning a evaluative tag of non applicable based on available data to a feature category if the value of an item in a product category does not change with numerical valuation of features within a feature category,

wherein the evaluative tag is used to rank items in a product category.

12. The method of claim 6 further comprising:

presenting a user with a choice of at least two feature categories for sorting; and

sorting items within a product category according to the user chosen feature categories, the weighted importance of all the feature categories and the evaluative metrics of the feature categories applied to the features within the feature categories.

13. The method of claim 1 further comprising:

deriving ranges of values within feature categories from the data records to determine natural ranges for grouping numerical features.

14. The method of claim 13 further comprising:

presenting a user with sub-ranges of values within feature categories for filtering product data to be presented.

15. The method of claim 1 further comprising:

applying statistical analysis to derive the placement of an item within a product category with respect to at least one feature category.

16. The method of claim 15 wherein statistical analysis is applied to derive the placement of an item within a product category with respect to two feature categories.

17. The method of claim 15 further comprising:

graphing the placement of an item within a product category.

18. The method of claim 17 further comprising:

presenting the user with a graph of the placement of a user chosen item with respect to other items in a product category based on the at least one feature category.

19. The method of claim 1 further comprising:

assigning a part of relation type if items in a product category are used only by inclusion in items in a second product category,

assigning a accessory relation type if items in a first product category are used only in conjunction with a second product category although the items in the second product category may be used without the items in the first product category,

assigning a resource relation type if items in a first product category are used only in conjunction with items in a second product category and must be replaced or replenished; and

creating links within each product category to reflect each assigned relation type.

20. The method of claim 19 further comprising:

creating hierarchical category trees using the kind of relation type assignments wherein each hierarchical category tree has one product category that is only a parent category.

21. The method of claim 1 further comprising:

assigning a weighted importance to buying information categories of product based on available data and ranking merchants offering products based on the weighted importance of the buying information categories.

22. The method of claim 21 further comprising:

assigning evaluative metrics to the buying information categories of the product categories and ranking the merchants offering each item based on the weighted importance of the buying information categories and evaluative metrics of the buying information categories.

23. The method of claim 21 wherein the buying information categories include price, shipping costs, shipping method and availability.

24. The method of claim 1 further comprising:

allowing a user to choose an item from a list of products in a product category; and

displaying the merchants selling the chosen products.

25. The method of claim 24 further comprising:

displaying buying information for each merchant selling the chosen products.

26. The method of claim 24 further comprising:

displaying the merchant buying information in a ranked list wherein the merchant buying information is ranked according to a weighted importance assigned to each buying information category and evaluative metrics applied to each buying information category based on the data records.

27. The method of claim 1, further including graphically rendering the user'"'"'s selections indicating importance of the plurality of feature categories to the user.

28. The method of claim 1, further including graphically rendering said plurality of feature categories for the user.